Inspired by the humans' ability to transfer knowledge from previous experience, transfer learning has become a promising technology in the field of machine learning to solve similar tasks with limited labeled data. This aims to improve the performance of target tasks by exploiting the knowledge from source tasks.
Recently, Yang et al. utilized a meta-learning scheme and effectively adapted the CSI estimation NN to the new environment [1]}, which provides a feasible way to apply transfer learning or meta-learning to the terminal extrapolation. Given the constraints on the performance of the terminal extrapolation, one should also determine which group should be selected to infer the NNs of the other groups. Moreover, the size of the group should be carefully designed to balance the performance of terminal extrapolation and training cost.
